Thermal analysis of extended surfaces using deep neural networks

IF 1.8 4区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY Open Physics Pub Date : 2024-07-29 DOI:10.1515/phys-2024-0051
Shina Daniel Oloniiju, Yusuf Olatunji Tijani, Olumuyiwa Otegbeye
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Abstract

The complexity of thermal analysis in practical systems has emerged as a subject of interest in various fields of science and engineering. Extended surfaces, commonly called fins, are crucial cooling and heating mechanisms in many applications, such as refrigerators and power plants. In this study, by using a deterministic approach, we discuss the thermal analysis of conduction, convection, and radiation in the presence of a magnetic force within an extended surface. The present study develops a deep neural network to analyze the mathematical model and to estimate the contributions of each dimensionless model parameter to the thermal dynamics of fins. The deep neural network used in this study makes use of a feedforward architecture in which the weights and biases are updated through backward propagation. The accuracy of the neural network model is validated using results obtained from a spectral-based linearization method. The efficiency rate of the extended surfaces is computed using the neural network and spectral methods. The results obtained demonstrate the accuracy of the neural network-based technique. The findings of this study in relation to the novel mathematical model reveal that utilizing materials with variable thermal conductivity enhances the efficiency rate of the extended surface.
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利用深度神经网络对扩展表面进行热分析
实用系统热分析的复杂性已成为科学和工程学各领域关注的主题。在冰箱和发电厂等许多应用中,扩展表面(通常称为翅片)是关键的冷却和加热机制。在本研究中,我们采用确定性方法,讨论了扩展表面内存在磁力时的传导、对流和辐射的热分析。本研究开发了一个深度神经网络来分析数学模型,并估算每个无量纲模型参数对鳍片热动力学的贡献。本研究中使用的深度神经网络采用前馈结构,通过后向传播更新权重和偏置。神经网络模型的准确性通过基于光谱的线性化方法得出的结果进行了验证。使用神经网络和光谱方法计算了扩展曲面的效率。所得结果证明了基于神经网络技术的准确性。这项与新型数学模型相关的研究结果表明,利用导热系数可变的材料可以提高扩展表面的效率。
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来源期刊
Open Physics
Open Physics PHYSICS, MULTIDISCIPLINARY-
CiteScore
3.20
自引率
5.30%
发文量
82
审稿时长
18 weeks
期刊介绍: Open Physics is a peer-reviewed, open access, electronic journal devoted to the publication of fundamental research results in all fields of physics. The journal provides the readers with free, instant, and permanent access to all content worldwide; and the authors with extensive promotion of published articles, long-time preservation, language-correction services, no space constraints and immediate publication. Our standard policy requires each paper to be reviewed by at least two Referees and the peer-review process is single-blind.
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